


'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
				import PIL

if is_torch_available():
				import torch


UpperCAmelCase_						:  List[str]				  =						logging.get_logger(__name__)





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Optional[Any]       =    ["""pixel_values"""]






							def __init__(       self       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = PILImageResampling.BILINEAR       ,   __snake_case = True       ,   __snake_case = 1 / 255       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = True       ,   **__snake_case       ,   ):
											super().__init__(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= size if size is not None else {"""shortest_edge""": 224}
											_SCREAMING_SNAKE_CASE					:			Dict							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
											_SCREAMING_SNAKE_CASE					:			int							= get_size_dict(__snake_case       ,   param_name="""crop_size"""    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= do_resize
											_SCREAMING_SNAKE_CASE					:			Dict							= size
											_SCREAMING_SNAKE_CASE					:			Dict							= resample
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_rescale
											_SCREAMING_SNAKE_CASE					:			int							= rescale_factor
											_SCREAMING_SNAKE_CASE					:			Any							= do_center_crop
											_SCREAMING_SNAKE_CASE					:			Any							= crop_size
											_SCREAMING_SNAKE_CASE					:			List[str]							= do_flip_channel_order






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = PIL.Image.BILINEAR       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											if "shortest_edge" not in size:
															raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""    )
											_SCREAMING_SNAKE_CASE					:			int							= get_resize_output_image_size(__snake_case       ,   size=size["""shortest_edge"""]       ,   default_to_square=__snake_case    )
											return resize(__snake_case       ,   size=__snake_case       ,   resample=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			int							= get_size_dict(__snake_case    )
											if "height" not in size or "width" not in size:
															raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
											return center_crop(__snake_case       ,   size=(size["""height"""], size["""width"""])       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return rescale(__snake_case       ,   scale=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None    ):
											return flip_channel_order(__snake_case       ,   data_format=__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = ChannelDimension.FIRST       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE					:			List[str]							= resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE					:			str							= do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE					:			Any							= rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE					:			int							= (
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

											_SCREAMING_SNAKE_CASE					:			Tuple							= size if size is not None else self.size
											_SCREAMING_SNAKE_CASE					:			Dict							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_size_dict(__snake_case       ,   param_name="""crop_size"""    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= make_list_of_images(__snake_case    )

											if not valid_images(__snake_case    ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""    )

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [to_numpy_array(__snake_case    ) for image in images]

											if do_resize:
															_SCREAMING_SNAKE_CASE					:			int							= [self.resize(image=__snake_case       ,   size=__snake_case       ,   resample=__snake_case    ) for image in images]

											if do_center_crop:
															_SCREAMING_SNAKE_CASE					:			Tuple							= [self.center_crop(image=__snake_case       ,   size=__snake_case    ) for image in images]

											if do_rescale:
															_SCREAMING_SNAKE_CASE					:			Any							= [self.rescale(image=__snake_case       ,   scale=__snake_case    ) for image in images]

											# the pretrained checkpoints assume images are BGR, not RGB
											if do_flip_channel_order:
															_SCREAMING_SNAKE_CASE					:			Tuple							= [self.flip_channel_order(image=__snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			str							= [to_channel_dimension_format(__snake_case       ,   __snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			Dict							= {"""pixel_values""": images}
											return BatchFeature(data=__snake_case       ,   tensor_type=__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
															if len(__snake_case    ) != len(__snake_case    ):
																			raise ValueError(
																			    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""    )

															if is_torch_tensor(__snake_case    ):
																			_SCREAMING_SNAKE_CASE					:			Dict							= target_sizes.numpy()

															_SCREAMING_SNAKE_CASE					:			Any							= []

															for idx in range(len(__snake_case    )    ):
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= torch.nn.functional.interpolate(
																			    logits[idx].unsqueeze(dim=0    )       ,   size=target_sizes[idx]       ,   mode="""bilinear"""       ,   align_corners=__snake_case    )
																			_SCREAMING_SNAKE_CASE					:			str							= resized_logits[0].argmax(dim=0    )
																			semantic_segmentation.append(__snake_case    )
											else:
															_SCREAMING_SNAKE_CASE					:			str							= logits.argmax(dim=1    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

											return semantic_segmentation








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




